GRACE: Graph-based Representation and Analysis for Crime Exploration

Criminal investigations increasingly rely on the analysis of vast amounts of unstructured textual data, such as crime reports, social media content, and open-source intelligence. While recent advances in Natural Language Processing (NLP) have shown promise in automating parts of this process, existing approaches often lack explicit, interactive knowledge representations that investigators can directly manipulate and enrich. In this paper, we envision Grace, a framework that combines automated information extraction, external data enrichment, and interactive visualization to support investigative workflows. Starting from unstructured crime reports, Grace applies named entity recognition and relation extraction to construct a structured knowledge graph, which is further augmented with contextual attributes from external sources. Investigators can explore and refine this evolving graph through an interactive interface that supports natural language querying, dynamic visualization of connections, and manual correction or enrichment of knowledge. To show the potential of Grace in supporting the investigation process, we provide a use case related to a fictional crime investigation. While still conceptual, our proposal aims to inspire the development of practical investigative tools that bridge automation and human expertise, ultimately enhancing the efficiency and effectiveness of criminal investigations.